Efficient RAG Framework for Large-Scale Knowledge Bases

Efficient RAG Framework for Large-Scale Knowledge Bases

Registration ID: IJNRD_219784

Published ID: IJNRD2404764

DOI: Click Here to Get

Karthik Meduri , Geeta Sandeep Nadella , Hari Gonaygunta , Mohan Harish Maturi , Farheen Fatima

LLM (Large Language Model), RAG Model, Knowledge Distillation, Quantization and Pruning Techniques, NLP (Natural Language Processing)

This research paper explores the nuances of optimizing large models of languages (LLMs) for the effective creation and retrieval of information. The current research investigation focuses on two main approaches: Knowledge Distillation (KD) and Retrieval-Augmented Generation (RAG), in addition to quantization and pruning strategies. KD reduces the size of LLMs without compromising functionality to maximize LLM efficiency and resource usage, whereas RAG combines external knowledge sources with LLMs to allow contextually relevant replies. LLMs are further optimized for constrained resource contexts through the use of quantization and trimming algorithms. By conducting a thorough assessment of the querying procedure, the research demonstrates the capacity of the model to produce precise answers and pinpoint areas in need of improvement. This study advances the architecture of the RAG Framework. It investigates its possibilities, providing large-scale scalable knowledge and practical solutions for knowledge creation and retrieval across a variety of fields, so opening the door for improved information access and human-machine interaction. This research will support the advancement of knowledge retrieval in all fields in the future.

Published Paper   E-Certificate

"Efficient RAG Framework for Large-Scale Knowledge Bases", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h613-h622, April-2024, Available :https://ijnrd.org/papers/IJNRD2404764.pdf

Volume 9 Issue 4, April-2024

Pages : h613-h622

Paper Reg. ID: IJNRD_219784

Published Paper Id: IJNRD2404764

Downloads: 000121239

Research Area: Computer Science & Technology 

Country: Stockton, California, United States

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404764.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404764

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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